Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process

Qunxiong Zhu; Ning Zhang; Yuan Xu; Yanlin He

doi:10.1109/DDCLS52934.2021.9455580

Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process

Qunxiong Zhu, Ning Zhang, Yuan Xu, Yanlin He

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

With the advancement of technology, the data collected by sensors have high-dimensional, non-linear characteristics. These data are difficult to be processed by traditional fault diagnosis methods. In this paper, an advanced fault diagnosis method based on discrimination neighborhood preserving embedding of Mahalanobis Distance (DNPE-M) was proposed. The proposed new method solves the problems of classification accuracy and data overlapping. Firstly, the high-dimensional and nonlinear data are dimensionally reduced by discrimination neighborhood preserving embedding based on the Mahalanobis Distance. Secondly, the fault data are classified using the integrated learning classifier AdaBoost. Finally, the Tennessee Eastman (TE) chemistry dataset is used to verify. The results of the experiments show that the proposed DNPE-M improves the performance of fault diagnosis accuracy.

Original language	English
Title of host publication	Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
Editors	Mingxuan Sun, Huaguang Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	18-22
Number of pages	5
ISBN (Electronic)	9781665424233
DOIs	https://doi.org/10.1109/DDCLS52934.2021.9455580
Publication status	Published - 14 May 2021
Externally published	Yes
Event	10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 - Suzhou, China Duration: 14 May 2021 → 16 May 2021

Publication series

Name	Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

Conference

Conference	10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021
Country/Territory	China
City	Suzhou
Period	14/05/21 → 16/05/21

Keywords

Discrimination Neighborhood Preserving Embedding
Fault Diagnosis
Mahalanobis Distance
Tennessee Eastman

Access to Document

10.1109/DDCLS52934.2021.9455580

Cite this

Zhu, Q., Zhang, N., Xu, Y., & He, Y. (2021). Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process. In M. Sun, & H. Zhang (Eds.), Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021 (pp. 18-22). Article 9455580 (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS52934.2021.9455580

Zhu, Qunxiong ; Zhang, Ning ; Xu, Yuan et al. / Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process. Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021. editor / Mingxuan Sun ; Huaguang Zhang. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 18-22 (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021).

@inproceedings{8a0fdf6e8a2c4a28b45ef928e5a3cf87,

title = "Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process",

abstract = "With the advancement of technology, the data collected by sensors have high-dimensional, non-linear characteristics. These data are difficult to be processed by traditional fault diagnosis methods. In this paper, an advanced fault diagnosis method based on discrimination neighborhood preserving embedding of Mahalanobis Distance (DNPE-M) was proposed. The proposed new method solves the problems of classification accuracy and data overlapping. Firstly, the high-dimensional and nonlinear data are dimensionally reduced by discrimination neighborhood preserving embedding based on the Mahalanobis Distance. Secondly, the fault data are classified using the integrated learning classifier AdaBoost. Finally, the Tennessee Eastman (TE) chemistry dataset is used to verify. The results of the experiments show that the proposed DNPE-M improves the performance of fault diagnosis accuracy.",

keywords = "Discrimination Neighborhood Preserving Embedding, Fault Diagnosis, Mahalanobis Distance, Tennessee Eastman",

author = "Qunxiong Zhu and Ning Zhang and Yuan Xu and Yanlin He",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 ; Conference date: 14-05-2021 Through 16-05-2021",

year = "2021",

month = may,

day = "14",

doi = "10.1109/DDCLS52934.2021.9455580",

language = "English",

series = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "18--22",

editor = "Mingxuan Sun and Huaguang Zhang",

booktitle = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",

address = "United States",

}

Zhu, Q, Zhang, N, Xu, Y & He, Y 2021, Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process. in M Sun & H Zhang (eds), Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021., 9455580, Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021, Institute of Electrical and Electronics Engineers Inc., pp. 18-22, 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021, Suzhou, China, 14/05/21. https://doi.org/10.1109/DDCLS52934.2021.9455580

Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process. / Zhu, Qunxiong; Zhang, Ning; Xu, Yuan et al.
Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021. ed. / Mingxuan Sun; Huaguang Zhang. Institute of Electrical and Electronics Engineers Inc., 2021. p. 18-22 9455580 (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process

AU - Zhu, Qunxiong

AU - Zhang, Ning

AU - Xu, Yuan

AU - He, Yanlin

PY - 2021/5/14

Y1 - 2021/5/14

N2 - With the advancement of technology, the data collected by sensors have high-dimensional, non-linear characteristics. These data are difficult to be processed by traditional fault diagnosis methods. In this paper, an advanced fault diagnosis method based on discrimination neighborhood preserving embedding of Mahalanobis Distance (DNPE-M) was proposed. The proposed new method solves the problems of classification accuracy and data overlapping. Firstly, the high-dimensional and nonlinear data are dimensionally reduced by discrimination neighborhood preserving embedding based on the Mahalanobis Distance. Secondly, the fault data are classified using the integrated learning classifier AdaBoost. Finally, the Tennessee Eastman (TE) chemistry dataset is used to verify. The results of the experiments show that the proposed DNPE-M improves the performance of fault diagnosis accuracy.

AB - With the advancement of technology, the data collected by sensors have high-dimensional, non-linear characteristics. These data are difficult to be processed by traditional fault diagnosis methods. In this paper, an advanced fault diagnosis method based on discrimination neighborhood preserving embedding of Mahalanobis Distance (DNPE-M) was proposed. The proposed new method solves the problems of classification accuracy and data overlapping. Firstly, the high-dimensional and nonlinear data are dimensionally reduced by discrimination neighborhood preserving embedding based on the Mahalanobis Distance. Secondly, the fault data are classified using the integrated learning classifier AdaBoost. Finally, the Tennessee Eastman (TE) chemistry dataset is used to verify. The results of the experiments show that the proposed DNPE-M improves the performance of fault diagnosis accuracy.

KW - Discrimination Neighborhood Preserving Embedding

KW - Fault Diagnosis

KW - Mahalanobis Distance

KW - Tennessee Eastman

UR - http://www.scopus.com/inward/record.url?scp=85114209234&partnerID=8YFLogxK

U2 - 10.1109/DDCLS52934.2021.9455580

DO - 10.1109/DDCLS52934.2021.9455580

M3 - Conference contribution

AN - SCOPUS:85114209234

T3 - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

SP - 18

EP - 22

BT - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

A2 - Sun, Mingxuan

A2 - Zhang, Huaguang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

Y2 - 14 May 2021 through 16 May 2021

ER -

Zhu Q, Zhang N, Xu Y, He Y. Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process. In Sun M, Zhang H, editors, Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 18-22. 9455580. (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021). doi: 10.1109/DDCLS52934.2021.9455580

Novel Mahalanobis Distance Based Fault Diagnosis Using Discrimination Neighborhood Preserving Embedding for Industrial Process

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this